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Development of a Novel Interpretable Transformer‐Based Deep Learning Model for Predicting Postoperative Hypokalemia in Pituitary Adenomas
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12
Autoren
2025
Jahr
Abstract
Our model outperforms XGBoost in predicting early postoperative hypokalemia in patients with pituitary adenomas. We systematically explore attention mechanisms in clinical tabular data, demonstrating their effectiveness in capturing complex feature interactions, leading to more individualized, interpretable, and clinically meaningful insights.
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